Datasets:

Voxel51
/

aimotive-multimodal

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 837 chars omitted)
  child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 825 chars omitted)
      child 0, _id: struct<$oid: string>
          child 0, $oid: string
      child 1, filepath: string
      child 2, tags: list<item: null>
          child 0, item: null
      child 3, _media_type: string
      child 4, _rand: double
      child 5, split: string
      child 6, condition: string
      child 7, episode_name: string
      child 8, session_id: string
      child 9, platform: string
      child 10, duration_s: double
      child 11, message_count: int64
      child 12, channel_count: int64
      child 13, topics: list<item: string>
          child 0, item: string
      child 14, schemas: list<item: string>
          child 0, item: string
      child 15, has_image: bool
      child 16, has_pointcloud: bool
      child 17, has_gps: bool
      child 18, has_imu: bool
      child 19, has_logs: bool
      child 20, has_3d_boxes: bool
      child 21, object_classes: list<item: string>
          child 0, item: string
      child 22, total_annotated_objects: int64
      child 23, avg_objects_per_frame: double
      child 24, num_annotated_frames: int64
      child 25, objects_by_class: struct<CAR: int64, TRUCK: int64, BUS: int64, RIDER: int64, MOTORCYCLE: int64, BICYCLE: int64, PEDEST (... 135 char
...
e: string, embedded_doc_type: string, subfield: string, fields:  (... 318 chars omitted)
  child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item:  (... 306 chars omitted)
      child 0, name: string
      child 1, ftype: string
      child 2, embedded_doc_type: string
      child 3, subfield: string
      child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
          child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
              child 0, name: string
              child 1, ftype: string
              child 2, embedded_doc_type: null
              child 3, subfield: null
              child 4, fields: list<item: null>
                  child 0, item: null
              child 5, db_field: string
              child 6, description: null
              child 7, info: null
              child 8, read_only: bool
              child 9, created_at: struct<$date: string>
                  child 0, $date: string
      child 5, db_field: string
      child 6, description: null
      child 7, info: null
      child 8, read_only: bool
      child 9, created_at: struct<$date: string>
          child 0, $date: string
frame_fields: list<item: null>
  child 0, item: null
slug: string
created_at: struct<$date: string>
  child 0, $date: string
persistent: bool
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 837 chars omitted)
                child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 825 chars omitted)
                    child 0, _id: struct<$oid: string>
                        child 0, $oid: string
                    child 1, filepath: string
                    child 2, tags: list<item: null>
                        child 0, item: null
                    child 3, _media_type: string
                    child 4, _rand: double
                    child 5, split: string
                    child 6, condition: string
                    child 7, episode_name: string
                    child 8, session_id: string
                    child 9, platform: string
                    child 10, duration_s: double
                    child 11, message_count: int64
                    child 12, channel_count: int64
                    child 13, topics: list<item: string>
                        child 0, item: string
                    child 14, schemas: list<item: string>
                        child 0, item: string
                    child 15, has_image: bool
                    child 16, has_pointcloud: bool
                    child 17, has_gps: bool
                    child 18, has_imu: bool
                    child 19, has_logs: bool
                    child 20, has_3d_boxes: bool
                    child 21, object_classes: list<item: string>
                        child 0, item: string
                    child 22, total_annotated_objects: int64
                    child 23, avg_objects_per_frame: double
                    child 24, num_annotated_frames: int64
                    child 25, objects_by_class: struct<CAR: int64, TRUCK: int64, BUS: int64, RIDER: int64, MOTORCYCLE: int64, BICYCLE: int64, PEDEST (... 135 char
              ...
              e: string, embedded_doc_type: string, subfield: string, fields:  (... 318 chars omitted)
                child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item:  (... 306 chars omitted)
                    child 0, name: string
                    child 1, ftype: string
                    child 2, embedded_doc_type: string
                    child 3, subfield: string
                    child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
                        child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
                            child 0, name: string
                            child 1, ftype: string
                            child 2, embedded_doc_type: null
                            child 3, subfield: null
                            child 4, fields: list<item: null>
                                child 0, item: null
                            child 5, db_field: string
                            child 6, description: null
                            child 7, info: null
                            child 8, read_only: bool
                            child 9, created_at: struct<$date: string>
                                child 0, $date: string
                    child 5, db_field: string
                    child 6, description: null
                    child 7, info: null
                    child 8, read_only: bool
                    child 9, created_at: struct<$date: string>
                        child 0, $date: string
              frame_fields: list<item: null>
                child 0, item: null
              slug: string
              created_at: struct<$date: string>
                child 0, $date: string
              persistent: bool
              to
              {'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
              because column names don't match

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Dataset Card for aiMotive Multimodal Dataset

The aiMotive Multimodal Dataset is a 176-scene autonomous driving dataset with synchronized and calibrated LiDAR, camera, and radar sensors providing 360-degree field-of-view coverage with sensor redundancy. Scenes were captured in highway, urban, and suburban environments across three countries during daytime, night, and rain. The dataset contains 26,583 annotated frames with 3D bounding boxes for 14 object classes (425k+ instances), ~24% of which are beyond 75 m from the ego vehicle, enabling long-range perception research. Parsed into FiftyOne multimodal format as 176 MCAP episodes with synchronized camera images, LiDAR point clouds, radar targets, GPS/IMU data, static sensor transforms, and per-frame 3D box annotations rendered as colored scene entities and projected 2D overlays.

Installation

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
dataset = load_from_hub("Voxel51/aimotive-multimodal")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Sources

Repository: https://github.com/aimotive/aimotive_dataset
Paper: https://arxiv.org/abs/2211.09445
Demo: [More Information Needed]

Uses

Direct Use

The dataset supports 3D object detection, multi-object tracking, and motion prediction research in autonomous driving. Its redundant 360-degree sensor layout (LiDAR + cameras + radars) and extended annotation range (up to 200 m) make it suited for training and benchmarking long-range perception models and multimodal sensor fusion methods. The presence of consistent track IDs across frames enables end-to-end tracking model development. GPS/IMU ego-motion data enables odometry and lead-car prediction tasks.

Out-of-Scope Use

The dataset must not be used for any military or harmful application. Commercial use is not permitted under the CC BY-NC-SA 4.0 license.

Dataset Structure

Overview

The FiftyOne dataset is a multimodal dataset containing 176 samples, one per 15-second episode. Each sample's filepath points to an MCAP file that encodes all sensor streams for that episode in a single time-indexed container. The dataset includes a train split (151 episodes, 21,402 frames) and a val split (25 episodes, 5,181 frames).

In the FiftyOne App, each sample opens the multimodal viewer with four synchronized tiles:

Image tile — per-camera JPEG streams with 3D-to-2D projected bounding box overlays
3D tile — LiDAR point cloud with colored 3D bounding box cubes (set reference frame to lidar)
Map tile — GPS trajectory derived from GNSS+INS
Message tile — raw JSON payloads for radar targets and annotation objects

Sample Fields

Field	FiftyOne type	Description
`filepath`	`StringField`	Absolute path to the episode MCAP file
`split`	`StringField`	`"train"` or `"val"`
`condition`	`StringField`	Recording environment: `"highway"`, `"night"`, `"rain"`, or `"urban"`
`episode_name`	`StringField`	Raw episode directory name, e.g. `"20210401-073402-00.18.00-00.18.15@Jarvis"`
`session_id`	`StringField`	Session timestamp prefix, e.g. `"20210401-073402"`
`platform`	`StringField`	Recording vehicle ID: `"Jarvis"`, `"Yoda"`, `"Cali"`, or `"Sogun"`
`duration_s`	`FloatField`	Episode duration in seconds (≈ 15 s)
`message_count`	`IntField`	Total number of MCAP messages in the episode
`channel_count`	`IntField`	Number of distinct MCAP channels in the episode
`topics`	`ListField(StringField)`	List of MCAP channel topic strings
`schemas`	`ListField(StringField)`	List of Foxglove schema names used in the episode
`has_image`	`BooleanField`	`True` for all 176 samples
`has_pointcloud`	`BooleanField`	`True` for all 176 samples
`has_gps`	`BooleanField`	`True` for all 176 samples
`has_3d_boxes`	`BooleanField`	`True` for all 176 samples
`object_classes`	`ListField(StringField)`	Sorted list of annotation class names present in this episode
`total_annotated_objects`	`IntField`	Total annotated object count summed across all frames
`avg_objects_per_frame`	`FloatField`	Mean annotated objects per frame for this episode
`num_annotated_frames`	`IntField`	Number of annotation frames in this episode
`objects_by_class`	`DictField`	Per-class object counts, e.g. `{"CAR": 842, "PEDESTRIAN": 17}`

MCAP Channels per Sample

Each MCAP file contains the following channels:

Topic	Foxglove schema	Viewer tile
`/camera/{front,back,left,right}/image_raw`	`foxglove.CompressedImage`	Image
`/camera/{front,back,left,right}/calibration`	`foxglove.CameraCalibration`	Image
`/camera/{front,back,left,right}/annotations`	`foxglove.ImageAnnotations`	Image (projected 2D box edges)
`/lidar/top`	`foxglove.PointCloud`	3D
`/annotations/scene`	`foxglove.SceneUpdate`	3D (colored bounding box cubes)
`/annotations/3d_boxes`	`aimotive.Annotations3D`	Message (raw JSON per frame)
`/radar/{front,back}`	`aimotive.RadarTargets`	Message (raw JSON per frame)
`/gps`	`foxglove.LocationFix`	Map
`/tf_static`	`foxglove.FrameTransform`	3D (sensor-to-body static transforms)

Annotation Classes

14 object classes: BICYCLE, BUS, CAR, MOTORCYCLE, OTHER-OBJECT, OTHER-RIDEABLE, PEDESTRIAN, PICKUP, RIDER, SHOPPING-CART, TRAILER, TRAIN, TRUCK, VAN.

3D Bounding Box Representation

Each annotated object carries: a 3D center point in the body coordinate frame (BoundingBox3D Origin X/Y/Z), full extents in meters (BoundingBox3D Extent X/Y/Z), orientation as a quaternion (BoundingBox3D Orientation Quat W/X/Y/Z), object class (ObjectType), and a persistent track ID (ObjectId). In the MCAP, these are written twice: as raw JSON on /annotations/3d_boxes (all fields verbatim) and as transformed CubePrimitive scene entities on /annotations/scene (origins and orientations re-expressed in the lidar sensor frame so they co-register with the LiDAR point cloud with no TF traversal).

Camera Sensor Details

The front and back cameras use the OpenCV pinhole model (5 distortion coefficients, plumb_bob). The left and right cameras use the equidistant fisheye model (4 distortion coefficients, equidistant). Back-camera resolution is 1920×1216; all other cameras are 1280×704. The 2D projected box overlays on each camera stream are computed by transforming 3D box corners from the body frame into each camera frame using the calibrated extrinsic matrix, then projecting with the camera-specific intrinsic model.

Platform Distribution

Platform	Episodes
Yoda	116
Jarvis	45
Cali	12
Sogun	3

Parsing Decisions

Timestamp alignment: Yoda-platform episodes derive Unix timestamps from egomotion2.json (time field, Unix seconds). Jarvis-platform episodes anchor device-boot nanosecond timestamps via the wall-clock start time in session.yml.
LiDAR transform: The LiDAR calibration entry provides pos_meter and yaw_pitch_roll_deg rather than a full 4×4 matrix; the extrinsic is reconstructed using ZYX Euler convention (vehicle standard).
Scene entity frame: 3D bounding box SceneUpdate entities are expressed in the lidar sensor frame (transformed from body frame at parse time) so that the reference frame lidar shows both geometry streams without requiring TF traversal.
LAZ filtering: LiDAR revolution files smaller than 226 bytes (empty/padding) and points within 0.5 m of the origin are discarded.

Dataset Creation

Curation Rationale

Existing autonomous driving datasets at the time of release lacked simultaneous sensor redundancy (coverage by at least two modalities) and extended annotation range. Most benchmarks annotate objects only within 80–100 m and do not include radar sensors, which limits robustness in adverse weather and the development of highway-capable long-range perception systems. The aiMotive dataset was created to address both gaps: a redundant 360-degree sensor layout (LiDAR + cameras + radars) combined with annotations extending to 200 m.

Source Data

Data Collection and Processing

Data was collected across three countries on two continents (California, USA; Austria; Hungary) using four instrumented vehicles (three Toyota Camry and one Toyota Prius). The recording phase spanned approximately one year to capture diverse seasons, weather conditions (sun, cloud, rain, glare), times of day (daytime, night), and geographic environments (highway, suburban, urban). Each scene is 15 seconds long. The dataset was anonymized using DashcamCleaner (face and license plate blurring). No additional preprocessing was applied to the raw sensor data beyond anonymization.

Who are the source data producers?

The data was collected and annotated by full-time aiMotive employees across the Vehicle Testing & Engineering, Calibration, aiNotate, and Object Detection teams. The data collection process satisfied requirements set by an Institutional Review Board approval and aiMotive's Data Protection Policy. No students, crowdworkers, or contractors were involved.

Annotations

Annotation process

Training annotations were generated automatically using a non-causal LiDAR-based tracker that accumulates point clouds across the full 15-second scene to fit 3D cuboids. The resulting annotations were manually quality-checked against thresholds of <10% position and size divergence and <5° orientation error. Scenes failing quality checks were discarded. Validation annotations were produced by human annotators using both LiDAR and camera data to fit cuboids, with the same orientation precision requirement. All annotated objects carry a persistent track ID consistent across all frames of a scene.

Who are the annotators?

Training data was annotated automatically and quality-checked by aiMotive employees. Validation data was labeled by hired human annotators and supervised through a two-phase review process, with final approval from a quality-checking supervisor.

Personal and Sensitive Information

Faces and license plates in camera images were blurred using DashcamCleaner prior to release. The raw unblurred data was not retained, in compliance with GDPR requirements.

Citation

BibTeX:

@misc{matuszka2022aimotivedatasetmultimodaldataset,
      title={aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception},
      author={Tamás Matuszka and Iván Barton and Ádám Butykai and Péter Hajas and Dávid Kiss and Domonkos Kovács and Sándor Kunsági-Máté and Péter Lengyel and Gábor Németh and Levente Pető and Dezső Ribli and Dávid Szeghy and Szabolcs Vajna and Bálint Varga},
      year={2022},
      eprint={2211.09445},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2211.09445},
}

APA:

Matuszka, T., Barton, I., Butykai, Á., Hajas, P., Kiss, D., Kovács, D., Kunsági-Máté, S., Lengyel, P., Németh, G., Pető, L., Ribli, D., Szeghy, D., Vajna, S., & Varga, B. (2022). aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception. arXiv:2211.09445.

More Information

The dataset DOI is 10.34740/kaggle/ds/2738461. The original dataset and baseline models (VoxelNet, BEVDepth, BEVFusion variants) are available at https://github.com/aimotive/aimotive_dataset. Contact: [email protected].